Journal of Signal Processing Systems

, Volume 74, Issue 3, pp 417–422 | Cite as

Symbolic Time Series Analysis of Temporal Gait Dynamics

Article

Abstract

Signals obtained from biological systems exhibit pronounced complexity. The patterns of change contain valuable information about the dynamics of underlying control mechanism of the complex biological systems. Human gait is a complex process with multiple inputs and numerous outputs. Various complexity analysis tools have been proposed to extract information from human gait time series. In this study, we used recently developed threshold based symbolic entropy to compare the spontaneous output of the human locomotors system during constrained and metronomically paced walking protocols. For that purpose, stride interval time series of healthy subjects who walked for 1 h at normal, slow and fast rates under different conditions was transformed into symbol sequences. Normalized corrected Shannon entropy (NCSE) was computed from the symbol sequences of the stride interval time series. The findings indicated that the unprompted output of human locomotors system is more complex during unconstrained normal walking as compared with slow, fast or metronomically paced walking.

Keywords

Gait dynamics Locomotors systems Shannon entropy Stride interval Symbolic entropy 

References

  1. 1.
    Costa, M., Penga, C. K., Goldbergera, L., & Hausdorf, J. M. (2003). Multiscale entropy analysis of human gait dynamics. Physica A, 330, 53–60.CrossRefMATHGoogle Scholar
  2. 2.
    Daw, C. S., Finney, C. E. A., Tracy, E. R. (2003). A review of symbolic analysis of experimental data. Review of Scientific Instruments, 74.Google Scholar
  3. 3.
    Peng, C. K., Hausdorf, J., & Goldberger, A. L. (2000). Fractal mechanisms in neuronal control: human heartbeat and gait dynamics in health and disease. In J. Walleczek (Ed.), Self-organized biological dynamics and nonlinear control (pp. 66–96). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  4. 4.
    Park, K. T., & Yi, S. H. (2004). Accessing physiological complexity of HRV by using threshold dependent symbolic entropy. Physical Socociety, 44(3), 569–576.Google Scholar
  5. 5.
    Eguia, M. C., Rabinovich, M. I., & Abarbanel, H. D. I. (2000). Information transmission and recovery in neural communication channels. Physical Review E, 62, 7111–7122.CrossRefGoogle Scholar
  6. 6.
    Madhavi, C. R., & Ananth, A. G. (2010). Quantification of Heart Rate Variability (HRV) data using symbolic entropy to distinguish between healthy and disease subjects. International Journal of Computer Applications, 8(12), 10–13.CrossRefGoogle Scholar
  7. 7.
    Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C. H., Mark, R. G., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220. http://circ.ahajournals.org/cgi/content/full/101/23/e215.CrossRefGoogle Scholar
  8. 8.
    Costa, M., Goldberger, A. L., & Peng, C.-K. (2002). Multiscale entropy analysis of complex physiologic time series. Physical Review Letters, 89(6), 1–4.Google Scholar
  9. 9.
    Aziz, W., & Arif, M. Complexity analysis of stride interval time series by threshold dependent symbolic entropy, European Journal of Applied Physiology, 98(1), 30–40.Google Scholar
  10. 10.
    Richman, J. S., & Moorman, J. R. (2000). The American Journal of Physiology, 278, H2039.Google Scholar
  11. 11.
    Hausdorff, J. M., Peng, C. K., Ladin, Z., Wei, J. Y., & Goldberger, A. (1994). Is walking a random walk? Evidence for long-range correlations in the stride interval of human gait. Journal of Applied Physiology, 78, 349–358.Google Scholar
  12. 12.
    Aziz, W., & Arif, M. Quantifying dynamics of physiological signals using non linear time series analysis techniques, ISBN 978-3-639-23544-9, paperback, 152 Pages.Google Scholar
  13. 13.
    Peng, C.-K., Hausdorf, J., & Goldberger, A. L. (2000). Fractal mechanisms in neuronal control: human heartbeat and gait dynamics in health and disease. In J. Walleczek (Ed.), Self-organized biological dynamics and nonlinear control (pp. 66–96). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  14. 14.
    Hausdorff, J. M., Cudkowicz, M. E., Firtion, R., Wei, J. Y., & Goldberger, A. L. (1998). Gait variability and basal ganglia disorders: stride-to stride variations of gait cycle timing in Parkinson’s and Huntington’s disease. Movement Disorders, 13, 428–437.CrossRefGoogle Scholar
  15. 15.
    West, B. J., & Scafetta, N. (2003). A nonlinear model for human gait. Physical Review E, 67, 051917.CrossRefMathSciNetGoogle Scholar
  16. 16.
    Scafetta, N., Griffin, L., & West, B. J. (2003). Hölder exponent spectra for human gait. Physica A, 328, 561–583.CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Scafetta, N., Marchi, D., & West, B. J. (2009). Understanding the complexity of human gait dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science, 19, 026108.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer Sciences and Information TechnologyUniversity of Azad Jammu and KashmirMuzaffarabadPakistan

Personalised recommendations